Cost performance analysis: Usage of resources in cloud using Markov-chain model

Jagannatha, S.; Shravan, N. S.; Kavya, S.

doi:10.1109/icaccs.2017.8014704

Cited by 6 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where π is a column vector containing all the steady-state probabilities P (u 1,1 ,,...,u M N ,N ) , the superscript T denotes the transposed operation, and 0 is a row vector with all elements equal to 0. The matrix Q is the state transition rate matrix, considering that rows define the origin state x and the columns the destination state y.The elements of Q, referred to q x,y , are defined as follows: The Gauss-Seidel method [27] has been selected to solve the SSBE system of equations (16) and compute the steadystate probabilities. This method avoids the discretization of the CTMC transition rate matrix and provides a good compromise between accuracy and complexity in comparison to other methods.…”

Section: B Model Implementationmentioning

confidence: 99%

“…In this context, this paper proposes a Markov model to characterize and assess the performance of RAN slicing in multi-service and multi-tenant scenarios. Markovian approaches have been widely used to characterize the utilization of resources in many fields, such as mobility [15], cloud computing [16], Call Admission Control (CAC) for 3G [17] and 4G femtocells [18] or for a heterogeneous network's Radio Access Technologies (RAT) policies [19]. More recently, works in the field of 5G exploit Markov modeling to approach a proactive resource allocation scheme in highly mobile networks [20] and the management of admission control for handoff requests between small-cell and macrocell domains [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Analytical Model for Multi-Tenant Radio Access Networks Supporting Guaranteed Bit Rate Services

et al. 2019

View full text Add to dashboard Cite

Network slicing is a key feature of forthcoming fifth generation (5G) systems to facilitate the partitioning of the network into multiple logical networks customized according to different business and application needs. Network slicing is a fundamental capability for enabling a cost-effective deployment and operation of 5G, as it allows the materialization of multi-tenant networks in which the same infrastructure is shared among multiple communication providers, each one using a different slice. This paper proposes a Markovian approach to characterize the resource sharing in multi-tenant scenarios with diverse guaranteed bit rate services by considering a slice-aware admission control policy. After describing the Markov model and its implementation and discussing its suitability, the model is applied to study the performance attained in a scenario with two different slices, one for enhanced mobile broadband communications and the other for mission critical services. The system is analyzed under standard and disaster situations, thus illustrating the capability to properly manage the different multi-tenant and multi-service traffic loads. INDEX TERMS Admission control, Markov processes, mobile communication, multi-tenancy, radio access networks, RAN slicing.

show abstract

Section: B Model Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Analytical Model for Multi-Tenant Radio Access Networks Supporting Guaranteed Bit Rate Services

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the above context, this paper tackles the RAN slicing problem from a modeling perspective by proposing and developing a Markov model characterization of RAN slicing in multi-tenant and multi-service scenarios. Markovian approaches have been widely used to characterize the utilization of resources in many fields, such as in mobility [24], cloud computing [25], Call Admission Control (CAC) scheme for 3G [26] or for heterogeneous networks Radio Access Technologies (RAT) policies [27]. More recently, works in the field of 5G exploit Markov modeling to approach a proactive resource allocation scheme in highly mobile networks [28], the management of Admission Control (AC) for handoff requests between small cell and macro cell domains [29], the computation of the estimated spectrum requirement [30] and the management of slices' creation [31].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Radio Access Network Slicing Scenarios With 5G QoS Provisioning

et al. 2020

View full text Add to dashboard Cite

5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning. INDEX TERMS Markov processes, radio access networks, RAN slicing, radio resource management, quality of service.

show abstract

“…Markovian approaches have been widely used to characterise the resource sharing paradigm in many fields, such as in mobility [12], cloud computing [13], Asynchronous Transfer Mode (ATM) dynamic capacity allocation [14] as well as in cellular networks (see e.g., [15] for a CDMA radio channel emulator, [16] for a Call Admission Control (CAC) scheme for 3G or [17] for heterogeneous networks Radio Access Technologies (RAT) policies). More recently, works in the field of 5G exploit Markov modelling for high mobility networks [18] [19].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, deg n S is defined as the union of the degraded states for the services of the n-th tenant, i.e. Equivalently the global system degraded states S deg would be computed as the union of the degraded states of each of the tenants.By using the previous definitions, the degradation probability per service and tenant is defined in(13). This can be easily extended to compute the degradation probability per tenant or the global degradation probability by considering deg n S or deg S in the summation, respectively.…”

mentioning

confidence: 99%

Guaranteed Bit Rate Traffic Prioritisation and Isolation in Multi-tenant Radio Access Networks

Vilà

Sallent

Umbert

et al. 2018

2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

View full text Add to dashboard Cite

Network slicing is a key feature of forthcoming 5G systems to facilitate the partitioning of the network into multiple logical networks customised according to different operation and application needs. Network slicing allows the materialisation of multi-tenant networks, in which the same infrastructure is shared among multiple communication providers, each one using a different slice. The support of multi-tenancy through slicing in the Radio Access Network (RAN) is particularly challenging because it involves the configuration and operation of multiple and diverse RAN behaviour over a common pool of radio resources while guaranteeing a certain Quality of Service (QoS) and isolation to each of the slices. This paper presents a Markovian approach to model different QoS aware Admission Control (AC) policies in a multi-tenant scenario with Guaranteed Bit Rate (GBR) services. From the analytical model, different metrics are defined to later analyse the effect of AC mechanisms on the performance achieved in various scenarios. Results show the impact of priorities for services of different tenants and isolation between tenants when different AC polices are adopted.

show abstract

Cost performance analysis: Usage of resources in cloud using Markov-chain model

Cited by 6 publications

References 8 publications

An Analytical Model for Multi-Tenant Radio Access Networks Supporting Guaranteed Bit Rate Services

An Analytical Model for Multi-Tenant Radio Access Networks Supporting Guaranteed Bit Rate Services

Characterization of Radio Access Network Slicing Scenarios With 5G QoS Provisioning

Guaranteed Bit Rate Traffic Prioritisation and Isolation in Multi-tenant Radio Access Networks

Contact Info

Product

Resources

About